The overall goal of this application is to develop an automated and quantitative analysis tool for brain MR images. The technology is based on the MriStudio program developed by Drs. Mori and Miller, which is characterized by accurate multi-modal diffeomorphic mapping and deformable atlases with extensive anatomical definitions of gray and white matter structures. The program has been extensively tested for accuracy in normal and various patient groups. This Phase I grant will support the integration of existing programs into an automated pipeline and generate data for FDA approval. Currently, daily radiological diagnosis of MRI is almost exclusively based on qualitative examination. However, the availability of quantitative analysis results, such as volumes of various brain structures, would provide a variety of benefits for clinical diagnosis and subsequent patient care. If quantitative reporting of anatomical status were available, it could be readily compared with results from normals to estimate the degree of abnormalities. Compared to the current free-text format, quantitative reporting could be correlated with clinical functions more easily. Quantitativ data could be stored as a part of clinical database (PACS), which is fully searchable, and, thus, past cases with similar anatomical status could be readily retrieved and the functional outcomes and final diagnosis in past cases could be used to enrich current diagnosis. If serial scans were available, longitudinal changes could also be appreciated readily. Our specific aims are; Aim 1: Build a pipeline for full automation and test the parcellation accuracy the newly designed tools will be based on the MriStudio platform (www.mristudio.org). This software is designed for research use, with full access to parameters and results at each analysis step. We need to convert it to a fully automated pipeline in a platform-independent manner. This new pipeline then must be rigorously tested for accuracy Dr. Mori's lab has 30 training image datasets with full manual segmentation for 12 basal ganglia and 16 core white matter structures. We will use these datasets to test the accuracy of the automated segmentation. Aim 2: Apply the pipeline to normal data and establish normal ranges of values for each age we will use the pediatric, young adult, and elderly normal databases in Dr. Mori's lab to establish normal values and the degree of anatomical variability at each age. We will quantify volumes, T2 intensity, and DTI- derived indices for each parcellated structure. The age-dependency of the quantified values and confidence levels will be characterized. This data will provide information about the statistical power to detect abnormalities. The database contains variability in imaging parameters, the impact of which on the measured values will be characterized. This information, as well as the existing clinical data for various brain diseases, will be used to evaluate the efficacy of the proposed tool in the Phase II study and in the future FDA application. PUBLIC HEALTH RELEVANCE: We will develop software for automated analysis of brain MR images. This software provides quantitative assessment of brain anatomical status of various brain disease patients.